pip install plotly pip install cufflinks

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  Using cached install-1.3.4-py3-none-any.whl (3.1 kB)
Collecting cufflinks
  Using cached cufflinks-0.17.3.tar.gz (81 kB)
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Using legacy 'setup.py install' for cufflinks, since package 'wheel' is not installed.
Installing collected packages: colorlover, install, cufflinks
    Running setup.py install for cufflinks: started
    Running setup.py install for cufflinks: finished with status 'done'
Successfully installed colorlover-0.3.0 cufflinks-0.17.3 install-1.3.4
Note: you may need to restart the kernel to use updated packages.

import pandas as pd
import numpy as np
import chart_studio.plotly as py
import cufflinks as cf
import seaborn as sns
import plotly.express as px
%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
cf.go_offline()

JNT

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
jntOne = pd.read_table('jnt-output-1.txt', delimiter = ":", header=None)
jntOne.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfOne = pd.DataFrame(jntOne)

# change column data type from string to int
jntOne['Count'] = pd.to_numeric(dfOne['Count'])

jntOne

	Word	Count
0	the	29
1	malaysian	1
2	communications	1
3	and	6
4	multimedia	1
...	...	...
123	choosing	1
124	service	2
125	delivery	1
126	purchases.	1
127	if:1\r\nthere:1\r\nloss:1\r\nitems,:1\r\nconta...	1

128 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
jntTwo = pd.read_table('jnt-output-2.txt', delimiter = ":", header=None)
jntTwo.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfTwo = pd.DataFrame(jntTwo)

# change column data type from string to int
jntTwo['Count'] = pd.to_numeric(dfTwo['Count'])

jntTwo

	Word	Count
0	over	1
1	the	12
2	past	1
3	few	2
4	weeks,	1
...	...	...
189	express	1
190	deserve	1
191	second	1
192	chance	1
193	malaysians?	1

194 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
jntThree = pd.read_table('jnt-output-3.txt', delimiter = ":", header=None)
jntThree.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfThree = pd.DataFrame(jntThree)

# change column data type from string to int
jntThree['Count'] = pd.to_numeric(dfThree['Count'])

jntThree

	Word	Count
0	the	38
1	up	5
2	and	34
3	coming	2
4	logistics	5
...	...	...
449	deals,	1
450	promos	1
451	and,	1
452	course,	1
453	shipping!�	1

454 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

#JNT WORD COUNT FOR ARCTICLE 1
px.histogram(jntOne.iloc[0:14], x='Word', y='Count', labels={'x': 'Word', 'y': 'Count'}, title='JNT Word Count - MCMC Issues Warning To J&T Express Over Video Showing Staff Mishandling Parcel')

#JNT WORD COUNT FOR ARCTICLE 2
px.histogram(jntTwo.iloc[0:14], x='Word', y='Count', labels={'x': 'Word', 'y': 'Count'}, title='JNT Word Count - J&T Express Experience Decline In Ordered Items Following Bad Publicity')

#JNT WORD COUNT FOR ARCTICLE 3
px.histogram(jntTwo.iloc[0:14], x='Word', y='Count', labels={'x': 'Word', 'y': 'Count'}, title='JNT Word Count - J&T Express anticipates promising 11.11 big sale')

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

jntOT = pd.merge(jntOne, jntTwo, on='Word', how= 'inner')

jnt = pd.merge(jntOT, jntThree, on= 'Word')

#rename the column
jnt.rename(columns = {'Count_x':'jnt-article-1', 'Count_y':'jnt-article-2',
                              'Count':'jnt-article-3'}, inplace = True)



px.bar(jnt, x='Word', y=["jnt-article-1", "jnt-article-2", "jnt-article-3"], title='JNT Word Count (INNER JOIN)')

ref: https://stackoverflow.com/questions/53645882/pandas-merging-101/53645883#53645883 https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html

To print to HTML

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
jntSW1 = pd.read_table('jnt-stopwords-1.txt', delimiter = ":", header=None)
jntSW1.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW1 = pd.DataFrame(jntSW1)

# change column data type from string to int
jntSW1['Count'] = pd.to_numeric(dfSW1['Count'])

jntSW1

	Word	Count
0	the	29
1	and	6
2	has	3
3	taken	1
4	of	7
5	that	6
6	showed	1
7	a	10
8	in	6
9	with	5
10	on	4
11	it	1
12	seriously	1
13	to	10
14	an	1
15	what	1
16	while	1
17	did	1
18	not	4
19	name	1
20	following	1
21	over	2
22	was	2
23	according	1
24	due	1
25	among	1
26	some	1
27	which	1
28	for	3
29	their	3
30	who	1
31	were	1
32	also	2
33	said	1
34	can	1
35	be	3
36	both	2
37	under	2
38	act	1
39	must	1
40	by	1
41	especially	1
42	could	1
43	more	1
44	than	1
45	or	3
46	three	1
47	if	2
48	they	2
49	are	3
50	found	1
51	have	1
52	any	3
53	before	1
54	there	1
55	is	1
56	may	1
57	via	1

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
jntSW2 = pd.read_table('jnt-stopwords-2.txt', delimiter = ":", header=None)
jntSW2.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW2 = pd.DataFrame(jntSW2)

# change column data type from string to int
jntSW2['Count'] = pd.to_numeric(dfSW2['Count'])

jntSW2

	Word	Count
0	over	1
1	the	12
2	past	1
3	few	2
4	there	2
...	...	...
69	could	1
70	do	1
71	you	1
72	think	1
73	second	1

74 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
jntSW3 = pd.read_table('jnt-stopwords-3.txt', delimiter = ":", header=None)
jntSW3.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW3 = pd.DataFrame(jntSW3)

# change column data type from string to int
jntSW3['Count'] = pd.to_numeric(dfSW3['Count'])

jntSW3

	Word	Count
0	the	38
1	up	5
2	and	34
3	a	21
4	in	21
...	...	...
92	help	1
93	together	1
94	can	1
95	best	1
96	us	1

97 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

jntSWOT = pd.merge(jntSW1, jntSW2, on='Word', how= 'inner')

jntSW = pd.merge(jntSWOT, jntSW3, on= 'Word')

#rename the column
jntSW.rename(columns = {'Count_x':'jnt-article-1', 'Count_y':'jnt-article-2',
                              'Count':'jnt-article-3'}, inplace = True)



px.bar(jntSW, x='Word', y=["jnt-article-1", "jnt-article-2", "jnt-article-3"], title='JNT Stopwords Count (INNER JOIN)')

CITYLINK

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
clOne = pd.read_table('citylink-output-1.txt', delimiter = ":", header=None)
clOne.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfOne = pd.DataFrame(clOne)

# change column data type from string to int
clOne['Count'] = pd.to_numeric(dfOne['Count'])

clOne

	Word	Count
0	city-link	12
1	express	6
2	maintains	1
3	its	3
4	fast	1
...	...	...
189	learning	1
190	techniques	1
191	economical	1
192	safe	1
193	driving.	1

194 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
clTwo = pd.read_table('citylink-output-2.txt', delimiter = ":", header=None)
clTwo.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfTwo = pd.DataFrame(clTwo)

# change column data type from string to int
clTwo['Count'] = pd.to_numeric(dfTwo['Count'])

clTwo

	Word	Count
0	city-link	2
1	express	1
2	(m)	1
3	sdn	1
4	bhd,	1
...	...	...
268	4.9ha	1
269	features	1
270	fully-automated	1
271	sorting	1
272	system.	1

273 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
clThree = pd.read_table('citylink-output-3.txt', delimiter = ":", header=None)
clThree.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfThree = pd.DataFrame(clThree)

# change column data type from string to int
clThree['Count'] = pd.to_numeric(dfThree['Count'])

clThree

	Word	Count
0	isuzu	6
1	malaysia	4
2	recently	2
3	delivered	2
4	a	6
...	...	...
213	features	1
214	achieving	1
215	economic	1
216	safety	1
217	driving.	1

218 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

clOT = pd.merge(clOne, clTwo, on='Word', how= 'inner')

cl = pd.merge(clOT, clThree, on= 'Word', how= 'inner')

#rename the column
cl.rename(columns = {'Count_x':'citylink-article-1', 'Count_y':'citylink-article-2',
                              'Count':'citylink-article-3'}, inplace = True)



px.bar(cl, x='Word', y=["citylink-article-1", "citylink-article-2", "citylink-article-3"], title='CityLink Word Count (INNER JOIN)')

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
clSW1 = pd.read_table('citylink-stopwords-1.txt', delimiter = ":", header=None)
clSW1.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW1 = pd.DataFrame(clSW1)

# change column data type from string to int
clSW1['Count'] = pd.to_numeric(dfSW1['Count'])

clSW1

	Word	Count
0	its	3
1	with	3
2	an	2
3	was	5
4	recently	2
5	at	4
6	the	13
7	up	1
8	in	9
9	to	10
10	of	9
11	line	1
12	has	1
13	been	1
14	as	4
15	seen	1
16	and	8
17	between	1
18	both	1
19	since	1
20	first	1
21	made	1
22	will	2
23	further	2
24	same	1
25	these	1
26	were	1
27	specifically	1
28	various	1
29	which	1
30	before	1
31	being	1
32	out	1
33	all	1
34	during	1
35	his	2
36	took	1
37	for	3
38	their	4
39	that	1
40	this	1
41	recent	1
42	a	1
43	also	1
44	on	1
45	are	1
46	well	2
47	by	1

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
clSW2 = pd.read_table('citylink-stopwords-2.txt', delimiter = ":", header=None)
clSW2.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW2 = pd.DataFrame(clSW2)

# change column data type from string to int
clSW2['Count'] = pd.to_numeric(dfSW2['Count'])

clSW2

	Word	Count
0	which	2
1	has	2
2	an	1
3	of	7
4	more	3
...	...	...
71	going	1
72	any	1
73	one	1
74	particular	1
75	least	1

76 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
clSW3 = pd.read_table('citylink-stopwords-3.txt', delimiter = ":", header=None)
clSW3.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW3 = pd.DataFrame(clSW3)

# change column data type from string to int
clSW3['Count'] = pd.to_numeric(dfSW3['Count'])

clSW3

	Word	Count
0	recently	2
1	a	6
2	of	16
3	their	4
4	to	8
5	during	1
6	an	2
7	at	1
8	in	6
9	the	20
10	eight	1
11	and	16
12	five	1
13	recent	1
14	is	2
15	between	1
16	both	2
17	since	1
18	its	3
19	first	2
20	made	1
21	back	1
22	has	1
23	further	1
24	line	1
25	with	2
26	throughout	1
27	while	2
28	also	2
29	saw	1
30	as	1
31	part	1
32	these	2
33	are	2
34	for	6
35	which	1
36	makes	1
37	it	1
38	his	1
39	he	1
40	that	1
41	will	2
42	be	1
43	overall	1
44	was	1
45	after	1
46	were	1
47	on	2
48	around	1
49	due	1
50	out	1

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

clSWOT = pd.merge(clSW1, clSW2, on='Word', how= 'inner')

clSW = pd.merge(clSWOT, clSW3, on= 'Word')

#rename the column
clSW.rename(columns = {'Count_x':'citylink-article-1', 'Count_y':'citylink-article-2',
                              'Count':'citylink-article-3'}, inplace = True)



px.bar(clSW, x='Word', y=["citylink-article-1", "citylink-article-2", "citylink-article-3"], title='CityLink Stopwords Count (INNER JOIN)')

DHL

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
dhlOne = pd.read_table('dhl-output-1.txt', delimiter = ":", header=None)
dhlOne.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfOne = pd.DataFrame(dhlOne)

# change column data type from string to int
dhlOne['Count'] = pd.to_numeric(dfOne['Count'])

dhlOne

	Word	Count
0	dhl	10
1	express	11
2	is	12
3	the	63
4	delivery	7
...	...	...
635	markets,�	1
636	announced	1
637	media	1
638	statement	1
639	wednesday.	1

640 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
dhlTwo = pd.read_table('dhl-output-2.txt', delimiter = ":", header=None)
dhlTwo.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfTwo = pd.DataFrame(dhlTwo)

# change column data type from string to int
dhlTwo['Count'] = pd.to_numeric(dfTwo['Count'])

dhlTwo

	Word	Count
0	although	1
1	globalisation	4
2	has	16
3	been	5
4	hit	1
...	...	...
632	markets,�	1
633	announced	1
634	media	1
635	statement	1
636	wednesday.	1

637 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
dhlThree = pd.read_table('dhl-output-3.txt', delimiter = ":", header=None)
dhlThree.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfThree = pd.DataFrame(dhlThree)

# change column data type from string to int
dhlThree['Count'] = pd.to_numeric(dfThree['Count'])

dhlThree

	Word	Count
0	companies	2
1	to	14
2	collaborate	1
3	on	2
4	the	23
...	...	...
315	methods	1
316	like	1
317	trucks	1
318	or	1
319	boats.	1

320 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

dhlOT = pd.merge(dhlOne, dhlTwo, on='Word', how= 'inner')

dhl = pd.merge(dhlOT, dhlThree, on= 'Word')

#rename the column
dhl.rename(columns = {'Count_x':'dhl-article-1', 'Count_y':'dhl-article-2',
                              'Count':'dhl-article-3'}, inplace = True)



px.bar(dhl, x='Word', y=["dhl-article-1", "dhl-article-2", "dhl-article-3"], title='DHL Word Count (INNER JOIN)')

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
dhlSW1 = pd.read_table('dhl-stopwords-1.txt', delimiter = ":", header=None)
dhlSW1.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW1 = pd.DataFrame(dhlSW1)

# change column data type from string to int
dhlSW1['Count'] = pd.to_numeric(dfSW1['Count'])

dhlSW1

	Word	Count
0	is	12
1	the	63
2	of	36
3	which	3
4	on	5
...	...	...
129	still	1
130	possible	1
131	always	1
132	eight	1
133	first	1

134 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
dhlSW2 = pd.read_table('dhl-stopwords-2.txt', delimiter = ":", header=None)
dhlSW2.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW2 = pd.DataFrame(dhlSW2)

# change column data type from string to int
dhlSW2['Count'] = pd.to_numeric(dfSW2['Count'])

dhlSW2

	Word	Count
0	although	1
1	has	16
2	been	5
3	because	1
4	of	35
...	...	...
129	still	1
130	possible	1
131	always	1
132	eight	1
133	first	1

134 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
dhlSW3 = pd.read_table('dhl-stopwords-3.txt', delimiter = ":", header=None)
dhlSW3.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW3 = pd.DataFrame(dhlSW3)

# change column data type from string to int
dhlSW3['Count'] = pd.to_numeric(dfSW3['Count'])

dhlSW3

	Word	Count
0	to	14
1	on	2
2	the	23
3	of	17
4	for	7
...	...	...
57	regardless	1
58	beyond	1
59	much	1
60	like	1
61	or	1

62 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

dhlSWOT = pd.merge(dhlSW1, dhlSW2, on='Word', how= 'inner')

dhlSW = pd.merge(dhlSWOT, dhlSW3, on= 'Word')

#rename the column
dhlSW.rename(columns = {'Count_x':'dhl-article-1', 'Count_y':'dhl-article-2',
                              'Count':'dhl-article-3'}, inplace = True)



px.bar(dhlSW, x='Word', y=["dhl-article-1", "dhl-article-2", "dhl-article-3"], title='DHL Stopwords (INNER JOIN)')

Poslaju

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
plOne = pd.read_table('poslaju-output-1.txt', delimiter = ":", header=None)
plOne.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfOne = pd.DataFrame(plOne)

# change column data type from string to int
plOne['Count'] = pd.to_numeric(dfOne['Count'])

plOne

	Word	Count
0	you	3
1	can	5
2	grab	1
3	a	31
4	burger	1
...	...	...
574	(see	1
575	stories	1
576	pages	1
577	36	1
578	37).	1

579 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
plTwo = pd.read_table('poslaju-output-2.txt', delimiter = ":", header=None)
plTwo.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfTwo = pd.DataFrame(plTwo)

# change column data type from string to int
plTwo['Count'] = pd.to_numeric(dfTwo['Count'])

plTwo

	Word	Count
0	recently,	1
1	i	7
2	ordered	1
3	an	2
4	item	1
...	...	...
156	work	1
157	make	1
158	malaysia	1
159	better	1
160	place.	1

161 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
plThree = pd.read_table('poslaju-output-3.txt', delimiter = ":", header=None)
plThree.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfThree = pd.DataFrame(plThree)

# change column data type from string to int
plThree['Count'] = pd.to_numeric(dfThree['Count'])

plThree

	Word	Count
0	in	5
1	a	6
2	press	1
3	statement	1
4	by	2
...	...	...
187	many.	1
188	not	1
189	excluded	1
190	from	1
191	crisis.	1

192 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

plOT = pd.merge(plOne, plTwo, on='Word', how= 'inner')

pl = pd.merge(plOT, plThree, on= 'Word')

#rename the column
pl.rename(columns = {'Count_x':'poslaju-article-1', 'Count_y':'poslaju-article-2',
                              'Count':'poslaju-article-3'}, inplace = True)



px.bar(pl, x='Word', y=["poslaju-article-1", "poslaju-article-2", "poslaju-article-3"], title='Pos Laju Word Count (INNER JOIN)')

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-1 using pandas
plSW1 = pd.read_table('poslaju-stopwords-1.txt', delimiter = ":", header=None)
plSW1.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW1 = pd.DataFrame(plSW1)

# change column data type from string to int
plSW1['Count'] = pd.to_numeric(dfSW1['Count'])

plSW1

	Word	Count
0	on	3
1	its	14
2	recent	1
3	of	14
4	the	23
...	...	...
59	resulted	1
60	overall	1
61	such	1
62	through	1
63	research	1

64 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-2 using pandas
plSW2 = pd.read_table('poslaju-stopwords-2.txt', delimiter = ":", header=None)
plSW2.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW2 = pd.DataFrame(plSW2)

# change column data type from string to int
plSW2['Count'] = pd.to_numeric(dfSW2['Count'])

plSW2

	Word	Count
0	i	7
1	an	2
2	which	1
3	was	2
4	to	13
...	...	...
62	more	1
63	into	1
64	our	1
65	make	1
66	better	1

67 rows × 2 columns

import plotly.express as px 
import pandas as pd 
import numpy as np

# read article-3 using pandas
plSW3 = pd.read_table('poslaju-stopwords-3.txt', delimiter = ":", header=None)
plSW3.columns = ['Word','Count']

# convert to 2d data structure using pandas
dfSW3 = pd.DataFrame(plSW3)

# change column data type from string to int
plSW3['Count'] = pd.to_numeric(dfSW3['Count'])

plSW3

	Word	Count
0	said	7
1	while	3
2	there	1
3	is	9
4	an	3
...	...	...
69	he	1
70	be	2
71	another	1
72	two	1
73	may	2

74 rows × 2 columns

import pandas as pd
import plotly.graph_objects as go
from plotly.offline import iplot

#Merge the table

plSWOT = pd.merge(plSW1, plSW2, on='Word', how= 'inner')

plSW = pd.merge(plSWOT, plSW3, on= 'Word')

#rename the column
plSW.rename(columns = {'Count_x':'poslaju-article-1', 'Count_y':'poslaju-article-2',
                              'Count':'poslaju-article-3'}, inplace = True)



px.bar(plSW, x='Word', y=["poslaju-article-1", "poslaju-article-2", "poslaju-article-3"], title='Pos Laju Stopwords (INNER JOIN)')